Low-power focal-plane dynamic texture segmentation based on programmable image binning and diffusion hardware

Stand-alone applications of vision are severely constrained by their limited power budget. This is one of the main reasons why vision has not yet been widely incorporated into wireless sensor networks. For them, image processing should be suscribed to the sensor node in order to reduce network traffic and its associated power consumption. In this scenario, operating the conventional acquisition-digitization-processing chain is unfeasible under tight power limitations. A bio-inspired scheme can be followed to meet the timing requirements while maintaining a low power consumption. In our approach, part of the low-level image processing is conveyed to the focal-plane thus speeding up system operation. Moreover, if a moderate accuracy is permissible, signal processing is realized in the analog domain, resulting in a highly efficient implementation. In this paper we propose a circuit to realize dynamic texture segmentation based on focal-plane spatial bandpass filtering of image subdivisions. By the appropriate binning, we introduce some constrains into the spatial extent of the targeted texture. By running time-controlled linear diffusion within each bin, a specific band of spatial frequencies can be highlighted. Measuring the average energy of the components in that band at each image bin the presence of a targeted texture can be detected and quantified. The resulting low-resolution representation of the scene can be then employed to track the texture along an image flow. An application specific chip, based on this analysis, is being developed for natural spaces monitoring by means of a network of low-power vision systems.Junta de Andalucía 2006-TIC-235Ministerio de Economía, Industria y Competitividad TEC 2006-1572

Automatic detection, tracking and counting of birds in marine video content

Robust automatic detection of moving objects in a marine context is a multi-faceted problem due to the complexity of the observed scene. The dynamic nature of the sea caused by waves, boat wakes, and weather conditions poses huge challenges for the development of a stable background model. Moreover, camera motion, reflections, lightning and illumination changes may contribute to false detections. Dynamic background subtraction (DBGS) is widely considered as a solution to tackle this issue in the scope of vessel detection for maritime traffic analysis. In this paper, the DBGS techniques suggested for ships are investigated and optimized for the monitoring and tracking of birds in marine video content. In addition to background subtraction, foreground candidates are filtered by a classifier based on their feature descriptors in order to remove non-bird objects. Different types of classifiers have been evaluated and results on a ground truth labeled dataset of challenging video fragments show similar levels of precision and recall of about 95% for the best performing classifier. The remaining foreground items are counted and birds are tracked along the video sequence using spatio-temporal motion prediction. This allows marine scientists to study the presence and behavior of birds

Segmentation of dynamic scenes with distributions of spatiotemporally oriented energies

In video segmentation, disambiguating appearance cues by grouping similar motions or dynamics is potentially powerful, though non-trivial. Dynamic changes of appearance can occur from rigid or non-rigid motion, as well as complex dynamic textures. While the former are easily captured by optical flow, phenomena such as a dissipating cloud of smoke, or flickering reflections on water, do not satisfy the assumption of brightness constancy, or cannot be modelled with rigid displacements in the image. To tackle this problem, we propose a robust representation of image dynamics as histograms of motion energy (HoME) obtained from convolutions of the video with spatiotemporal filters. They capture a wide range of dynamics and handle problems previously studied separately (motion and dynamic texture segmentation). They thus offer a potential solution for a new class of problems that contain these effects in the same scene. Our representation of image dynamics is integrated in a graph-based segmentation framework and combined with colour histograms to represent the appearance of regions. In the case of translating and occluding segments, the proposed features additionally serve to characterize the motion of the boundary between pairs of segments, to identify the occluder and inferring a local depth ordering. The resulting segmentation method is completely modelfree and unsupervised, and achieves state-of-the-art results on the SynthDB dataset for dynamic texture segmentation, on the MIT dataset for motion segmentation, and reasonable performance on the CMU dataset for occlusion boundaries.</p

Dense Motion Estimation for Smoke

Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skeletal flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.Comment: ACCV201

Fuzzy Recursive Least-Squares Approach in Speech System Identification: A Transformed Domain LPC Model

In speech system identification, linear predictive coding (LPC) model is often employed due to its simple yet powerful representation of speech production model. However, the accuracy of LPC model often depends on the number and quality of past speech samples that are fed into the model; and it becomes a problem when past speech samples are not widely available or corrupted by noise. In this paper, fuzzy system is integrated into the LPC model using the recursive least-squares approach, where the fuzzy parameters are used to characterize the given speech samples. This transformed domain LPC model is called the FRLS-LPC model, in which its performance depends on the fuzzy rules and membership functions defined by the user. Based on the simulations, the FRLS-LPC model with this special property is shown to outperform the LPC model. Under the condition of limited past speech samples, simulation result shows that the synthetic speech produced by the FRLS-LPC model is better than those produced by the LPC model in terms of prediction error. Furthermore with corrupted past speech samples, the FRLS-LPC model is able to provide better reconstructed speech while the LPC model is failed to do so

Motion Textures: Modeling, Classification, and Segmentation Using Mixed-State Markov Random Fields

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Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation

Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Often, an object detector requires manually labeled examples to train a binary classifier, while background subtraction needs a training sequence that contains no objects to build a background model. To automate the analysis, object detection without a separate training phase becomes a critical task. People have tried to tackle this task by using motion information. But existing motion-based methods are usually limited when coping with complex scenarios such as nonrigid motion and dynamic background. In this paper, we show that above challenges can be addressed in a unified framework named DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR). This formulation integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm efficiently. We explain the relations between DECOLOR and other sparsity-based methods. Experiments on both simulated data and real sequences demonstrate that DECOLOR outperforms the state-of-the-art approaches and it can work effectively on a wide range of complex scenarios.Comment: 30 page